通过r-indexing对eBWT进行指数化
Christina Boucher1, Davide Cenzato2, Zsuzsanna Lipták2
1Dept of Computer and Information Science and Engineering, University of Florida.
International Symposium on String Processing and Information Retrieval : SPIRE ... : proceedings. SPIRE (Symposium)
|May 14, 2024
概括
本研究提出了一种有效的r-indexing方法,用于使用前免费解析的扩展Burrows Wheeler转换 (eBWT). 这种方法可以更快地构建压缩的eBWT表示,并有效地搜索最大精确匹配 (MEM).
科学领域:
- 生物信息学是一种生物信息学.
- 字符串算法 字符串算法
- 数据压缩数据压缩
背景情况:
- 扩展的Burrows Wheeler转换 (eBWT) 将Burrows Wheeler转换概括为字符串的集合.
- 之前的工作为eBWT构建建立了线性时间算法,保持了输入顺序独立性.
- 这个算法集成了后数组诱导排序 (SAIS) 和前自由解析 (PFP) 技术.
研究的目的:
- 展示eBWT构建算法如何促进高效的r-indexing.
- 为了显示运行长度编码的eBWT的构建,并从PFP组件中取样后数组 (SA).
- 建立有效的支持,以使用r-索引的eBWT找到最大精确匹配 (MEM).
主要方法:
- 利用基于SAIS和PFP的线性时间eBWT构建算法.
- 使用PFP的组件来构建r指数的eBWT.
- 在最大精确匹配 (MEM) 查询中应用r索引的eBWT结构.
主要成果:
- 从PFP组件有效构建r指数的eBWT.
- 演示运行长度编码的eBWT和SA样本的高效计算.
- 有效的支持找到最大精确匹配 (MEMs) 与r-索引的eBWT.
结论:
- 拟议的方法提供了一种有效的方法来重新索引eBWT.
- 这使得序列分析和数据压缩的实际应用成为可能.
- 该框架有效地支持关键字符串匹配操作,如MEM识别.
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